Human motion database with a binary tree and node transition graphs

Authors:
Katsu Yamane;Yoshifumi Yamaguchi;Yoshihiko Nakamura
Affiliations:
Disney Research, Pittsburgh, USA;Dept. of Mechano-Informatics, University of Tokyo, Tokyo, Japan and Oracle Corporation Japan, Tokyo, Japan;Dept. of Mechano-Informatics, University of Tokyo, Tokyo, Japan
Venue:
Autonomous Robots
Year:
2011

Citing 9
Cited 0

Minimum error thresholding

Pattern Recognition
Multidimensional Balanced Binary Trees

IEEE Transactions on Computers
Style machines

Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Motion graphs

Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Interactive control of avatars animated with human motion data

Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Implicit Probabilistic Models of Human Motion for Synthesis and Tracking

ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Incremental PCA or On-Line Visual Learning and Recognition

ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Construction and optimal search of interpolated motion graphs

ACM SIGGRAPH 2007 papers
Incremental Learning, Clustering and Hierarchy Formation of Whole Body Motion Patterns using Adaptive Hidden Markov Chains

International Journal of Robotics Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

Database of human motion has been widely used for recognizing human motion and synthesizing humanoid motions. In this paper, we propose a data structure for storing and extracting human motion data and demonstrate that the database can be applied to the recognition and motion synthesis problems in robotics. We develop an efficient method for building a human motion database from a collection of continuous, multi-dimensional motion clips. The database consists of a binary tree representing the hierarchical clustering of the states observed in the motion clips, as well as node transition graphs representing the possible transitions among the nodes in the binary tree. Using databases constructed from real human motion data, we demonstrate that the proposed data structure can be used for human motion recognition, state estimation and prediction, and robot motion planning.